A direct interactive modulation method for RWSR is to train several models with different degradation levels. The modulation can then be achieved by performing inference on those models and selecting the most satisfactory restoration output.
To compare our method with this case, we uniformly divide the random high-order degradations in training Real-ESRGAN [1]} into four segments, corresponding to four models.
In the evaluation, we select the best result (lowest LPIPS) among the four results for each sample.
We experiment on the RealSRSet test set [2]} and present the quantitative results in Tab. REF . The visual comparison is presented in Fig. REF . One can see that the performance of such a simple approach is inferior to our method both quantitatively and qualitatively. Besides, this simple method only has four points for modulation.
